1. CROWDSOURCING AND GI
JAVIER MORALES

2. AGENDA
CROWDSOURCING AND SPATIAL DATA INFRASTRUCTURES
 Background
 Crowdsourcing Principles
 Examples
 Conclusions
© Manuel Ramos
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 2

3. BACKGROUND
THE ROLE OF GI
Geographic information (GI) was for generations produced and
consumed by professionals
Societal processes
 land transfer,
 planning and development,
 risk management
 …
that affect organisations and individuals.
Trend to develop mechanisms to bring GI closer to non-professional
users
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 3

4. BACKGROUND
MODERN TOOLS
 Web 2.0
 high interactivity,
 sharing and collaboration,
 Interoperability, and
 real-time user-generated content
 Web 2.0 apps
 social networking,
 blogging,
 wikis,
 video sharing, and
 Mashups
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 4

5. BACKGROUND
WEB 2.0
The users’ role has changed
from looking for and retrieving content  to active participation
© www.techscreens.com
everyone contributes to the common knowledge
of the group they interact with
Wikipedia, YouTube, Flickr, Wikimedia
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 5

6. CROWDSOURCING
WHY?
 Organisations today have to operate in information-rich environments
 They can no longer afford to rely entirely on their own ideas
 They cannot bet their success to a single product to the market
 Traditional development which largely focused on
 intra-organisational skills,
 closed off from outside ideas and technologies
is becoming obsolete
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 6

7. CROWD-WHAT?
 Crowdsourcing
is the act of taking a job traditionally performed by a designated
agent (usually an employee) and outsourcing it to an undefined,
generally large group of people in the form of an open call.
Jeff Howe
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 7

8. CROW-WHAT?
 The crowdsourcing approach
 a recognised entity posts a problem online
 a large number of individuals reacts
 they provide a small part of the solution to the problem
 solutions offered are exhaustive and not disjoint
 This approach is popular because
 web-based social technology makes it feasible & affordable to collect
data using groups of individuals
 such data is often more accurate indicator of current conditions in the
real world than what can be obtained from data stored in databases
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 8

9. CROWDSOURCING PRINCIPLES
1. Formulate the problem properly
 Scope & purpose
2. State deliverables concretely (quality)
 let the crowd know exactly what is expected from them
 leave space for their creativity
3. Connect with the right crowd
 diversity (the question is answered from multiple points of view)
 scientists or specialists and a significant number of hobbyists
with knowledge in the problem domain
4. Deploy the appropriate crowd management scheme
 moderate discussion boards
 post provocative challenges & publish milestones
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 9

10. EXAMPLES
 Ushahidi
 GeoNames
 Geonode
 Google MapMaker
 OpenStreetMaps
Aim at providing open data through the
Creative Commons Attribution – ShareAlike license
data can be used freely and if you alter or build upon it,
you need to share those alterations back to the community
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 10

11. OPEN DATA
Data is considered to be open if
 it is and publish online,
 updated as often as possible,
 provided in a way that allows for its legal use for any purpose, and
 that allows easy processing with any arbitrary software program
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 11

12. OPENSTREETMAP
 The OpenStreetMap project is a crowdsourced geospatial data
repository, with a global cast of volunteers.
 With the mission to create a free editable dataset of the world
 It has been very successful especially
 In producing data fro places where it was very scarce
(rural & peri-urban areas)
 In keeping up-to-date datasets of rapidly evolving urban areas
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 12

13. OPENSTREETMAP
A YEAR OF EDITS
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14. OPENSTREETMAP
PROJECT HAITI - 2010
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15. OPENSTREETMAP
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 15

16. SPATIAL DATASETS
CROWDSOURCING IMPACT
Chia, Colombia
Maps
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17. OPENSTREETMAP
COMPARISON
Enschede
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18. OPENSTREETMAP
COMPARISON
Guatemala
City
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 18

19. SPATIAL DATASETS
CROWDSOURCING IMPACT
Lahore, Pakistan in Lahore, Pakistan in
Google Maps Google Maps
(before MapMaker) (after MapMaker)
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 19

20. SPATIAL DATASETS
CROWDSOURCING IMPACT
Bolivia
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 20

21. USHAHIDI
HISTORY
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 21

22. USHAHIDI
WORKING APPROACH
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 22

23. USHAHIDI
DATA INPUTS
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 23

24. USHAHIDI
EXPLOITATION
Disaster Response
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25. USHAHIDI
EXAMPLES http://ushahidi.internewskenya.org/
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 25

26. USHAHIDI
EXAMPLES http://haiti.ushahidi.com/
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27. CONCLUSIONS
CROWDS & SDI
 Work on something relevant
(or at least has the promise of being useful relatively soon)
 Put the users at the center
 View users as important contributors
 Give them responsibility
 Enable ratings
 Derive metadata from usage
 Make customization as easy as possible
 Enable mashups
 Unlock the visualizations
 Index your data and become searchable
© Department of Geo-information Processing (GIP) – 27-Oct-2011 – 27

28. CHALLENGES
RESEARCH ISSUES
 Automatic validation an filtering of data inputs
 Indirect geo-tagging (mining of social networks)
 Automatic aggregation & summarizing of similar data entries

© Community FixIt
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29. © Department of Geo-information Processing (GIP) – 27-Oct-2011 – 29